A Crop–Weather Model for Prediction of Rice (Oryza sativa L.) Yield Using an Empirical‐Statistical Technique

Abstract

Rice is the staple food in many countries and is grown in varied climates from per‐humid to semiarid areas. Crop–weather models were used to predict rice yield in India. However, in spite of a significant influence of solar radiation on rice yield, none of these models used solar radiation as one of the predictors. In this paper, an attempt was made to predict the first season (June–September) rice yield at Coimbatore, Tamil Nadu, India by including solar radiation as one of the predictors. Ten years (1987/88–1996/97) data were used for the study. Seven predictors viz., percentage of rice area during first season (X1), number of days with minimum temperature below 22 °C in August and September (X2), average daily maximum temperature for three months (July, August and September; X3), average daily minimum temperature for three months (July, August and September; X4), total of average sunshine hours in August and September (X5), and total rainfall of July, August and September (X6) total average solar radiation of August and September (X7) were selected based on earlier report. Full model and stepwise regression analysis were performed using MSTAT computer package. The full model regression without solar radiation as predictor (Model I) recorded comparatively less R2 (0.6292). Inclusion of solar radiation (Model II) enhanced the R2 value considerably (R2=0.9464). Seven variables were further subjected to stepwise regression analysis and only four predictors were retained in the final model (Model III) with an R2 value of 0.9234. The model III with minimum parameters Y=22119.5758 + 19.6898, X1 − 150.9261, X2 − 1126.7501, X4 + 0.7179 X7 can be used to predict the first season rice yield (Y) at Coimbatore, India.

Identification of Most Important Weeks on Minimum Temperature for Wheat Crop

“…Almost similar results, as observed in this study, have also been reported by Kandiannan et al, (2002) for Coimbatore in Tamil Nadu, where temperature, rainfall and radiation entered significantly in a stepwise prediction equation of rice yield. In Andhra Pradesh also, rainfall and temperature have been reported to affect rice yield significantly (Barnwal and Kotani, 2010).…”

Estimativa da produtividade de arroz irrigado em função da radiação solar global e da temperatura mínima do ar

“…Esse método tem sido usado para estimar a produtividade de grãos de arroz em diferentes regiões do mundo, como no Japão, considerando horas de insolação durante o período de pré-floração e enchimento de grão (MURATA, 1975), em diferentes localidades da África, Ásia, América Latina e Oceania, utilizando a radiação solar global e a temperatura mínima do ar nos períodos de pré e pós-floração (SESHU & CADY, 1984), e na Índia, usando, dentre outras variáveis, a radiação solar, a insolação, a temperatura mínima do ar e a temperatura máxima do ar (KANDIANNAN et al, 2002).…”

Study on the Interactions Between Lathyrus (Lathyrus sativus L.) and Agro-climatic Factors to Generate Weather Based Yield Forecasting Models

“…Development of regression models for prediction of crop yield with the help of agro-climatic factors prevailing during the crop growing season was reported earlier (Kandiannan et al, 2002, Sharma et al, 2004. Forecasting of crop yield before harvest was also documented previously (Smith and Gooding., 1999).…”